The previous adversarial learning is generally limited to the labels without the target domain, so CDAN [1]} proposes to jointly consider the feature representation and the prediction result of the classifier in the discriminator part. CDAN believes that the outer product of the feature representation and the prediction result can affect the feature representation, so a discriminator shared by the source domain and the target domain is used to align this conditional feature representation. The general conditional adversarial loss are as follows:
\(\begin{aligned}\mathcal {L}_d^{con}\left(F,D\right)=-\mathbb {E}_{x_s~P_s}\log {\left[D\left(f_s,p_s\right)\right]}-\mathbb {E}_{x_t~Q_t}\log {\left[1-D\left(f_t,p_t\right)\right]}\end{aligned}.\) 
